System and method for age-based gamut mapping

ABSTRACT

A method for processing an input image having an initial gamut to a targeted image having a wider gamut for display on a wide-gamut display includes determining a set of color scaling factors based on different parameters including one or more user-related characteristics of a user and the available gamut of the wide-gamut display device, applying a gamut-mapping to the input image based on the available gamut to generate a gamut-mapped image and applying the set of color scaling factors to the gamut-mapped image to generate the targeted image. A user device having a wide-gamut display device also includes an image processing module configured for performing the method for processing the input image.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patent application Ser. No. 16/347,871, which is a National Stage of International Application no. PCT/CA2017/051321 filed on Nov. 7, 2017, which claims priority to U.S. Provisional Application No. 62/418,361 filed on Nov. 7, 2016.

TECHNICAL FIELD

The technical field generally relates to processing of images for displaying onto a wide-gamut display device.

BACKGROUND

Colorimetry is based on the assumption that everyone's color response can be quantified with the CIE standard observer functions, which predict the average viewer's response to the spectral content of light. However, individual observers may have slightly different response functions, which may cause disagreement about which colors match and which do not. For colors with smoothly varying (broad) spectra, the disagreement is generally small, but for colors mixed using a few narrow-band spectral peaks, differences can be as large as 10 CIELAB units [Fairchild & Wyble 2007]. (Anything greater than 5 CIELAB units is highly salient.)

Wide-gamut displays, such as organic light-emitting diodes (OLEDs), can amplify this problematic situation. This makes it difficult for observers to agree on what constitutes white on narrow-band displays such as Samsung's popular AMOLED devices. Observer metamerism is likely to occur more frequently with wide color gamut.

SUMMARY OF THE INVENTION

According to one aspect, there is provided a method for processing an input image having an initial gamut to a targeted image having a targeted gamut which is wider than said initial gamut for display on a wide-gamut display device having an available gamut which is wider than said initial gamut, the input image having color value components of pixels. The method includes receiving said input image, determining a set of color scaling factors based on different parameters comprising one or more user-related characteristics of a user of said wide-gamut display device and said available gamut of the wide-gamut display device, applying a gamut-mapping to the input image, based on said available gamut of the wide-gamut display device, to generate a gamut-mapped image, and applying the set of color scaling factors to the gamut-mapped image to generate said targeted image.

According to another aspect, there is provided a user device that includes a wide gamut display device configured for displaying images, a computer program in which various graphical content is to be displayed on the wide-gamut display device, said user device being configured to execute said computer program, an image processing module implemented within the user device to generate a processed graphical content adapted to the user, and wherein said image processing module is configured to perform the image processing method as described in for generating said processed graphical content for display on said wide-gamut display device.

According to various aspects, a computer-implemented system includes at least one data storage device; and at least one processor operably coupled to the at least one storage device, the at least one processor being configured for performing the methods described herein according to various aspects.

According to various aspects, a computer-readable storage medium includes computer executable instructions for performing the methods described herein according to various aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

While the above description provides examples of the embodiments, it will be appreciated that some features and/or functions of the described embodiments are susceptible to modification without departing from the spirit and principles of operation of the described embodiments. Accordingly, what has been described above has been intended to be illustrative and non-limiting and it will be understood by persons skilled in the art that other variants and modifications may be made without departing from the scope of the invention as defined in the claims appended hereto.

FIG. 1 illustrates a schematic diagram of the operational modules of a system for white balancing/gamut expansion;

FIG. 2 illustrates a flowchart of the operational steps of an exemplary method for processing an input image for display on a wide-gamut display device;

FIG. 3 illustrates a flowchart of the operational steps of an exemplary method for determining color scaling factors for shifting a white point;

FIG. 4 illustrates a flowchart of the operational steps of an example method for applying gamut-mapping to an input image;

FIG. 5 illustrates a system for user-adapted display of graphical content from a content provider;

FIG. 6 illustrates a flowchart of the operational steps of a method for user-adapted display of graphical content from a content provider;

FIG. 7 illustrates the difference in D65 white appearance relative to a 25 year-old reference subject on a Samsung AMOLED display (Galaxy Tab) for 2 degree and 10 degree patches;

FIG. 8 illustrates the sacred region (green) with a line drawn from center through input color to sRGB gamut boundary in chromaticity space;

FIG. 9 illustrates a mapping from an sRGB gamut to AMOLED primaries showing example color motions using the example implementation described herein;

FIG. 10 illustrates a mapping from an sRGB gamut to laser primaries showing example color motions using the example implementation described herein;

FIG. 11 illustrates examples image of an image in sRGB input (top) and the image white-balance and gamut-mapped using the example implementation described herein using laser display on bottom. Intense colors become more intense, and some shift slightly in hue, especially in deep blue where primaries do not align.

FIG. 12 illustrates Gamut mapping examples with original images and colorimetric reference: HCM—the example implementation described herein, SDS—original image, and TCM—colorimetric or true color mapping.

FIG. 13 illustrates a graph of Subjective evaluation results of pairwise comparison representing as JND values for each of 10 images including error bars which denote 95% confidence intervals calculated by bootstrapping —HCM: the example implementation described herein, SDS:original image, and TCM:colorimetric or true color mapping.

DETAILED DESCRIPTION

Broadly described, various example embodiments described herein provide for processing of an input image, which may be represented in a standard color space, according to a user-related characteristic, such as age, so as to display the image, for example, on a wide-gamut display device.

One or more gamut mapping systems described herein may be implemented in computer programs executing on programmable computers, each comprising at least one processor, a data storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. For example, and without limitation, the programmable computer may be a programmable logic unit, a mainframe computer, server, and personal computer, cloud based program or system, laptop, personal data assistance, cellular telephone, smartphone, wearable device, tablet device, virtual reality devices, smart display devices (ex: Smart TVs), set-top box, video game console, or portable video game devices.

Each program is preferably implemented in a high level procedural or object oriented programming and/or scripting language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Each such computer program is preferably stored on a storage media or a device readable by a general or special purpose programmable computer for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. In some embodiments, the systems may be embedded within an operating system running on the programmable computer. In other example embodiments, the system may be implemented in hardware, such as within a video card.

Furthermore, the systems, processes and methods of the described embodiments are capable of being distributed in a computer program product comprising a computer readable medium that bears computer-usable instructions for one or more processors. The medium may be provided in various forms including one or more diskettes, compact disks, tapes, chips, wireline transmissions, satellite transmissions, internet transmission or downloadings, magnetic and electronic storage media, digital and analog signals, and the like. The computer-usable instructions may also be in various forms including compiled and non-compiled code.

A challenge is to provide the best viewer experience on wide-gamut display devices by customizing the color mapping to account for individual preference and physiological traits. Despite the predominant use of the sRGB standard color space, which has a rather limited gamut, images in such color space should be processed in a way that takes advantage of the additional gamut provided by wide-gamut display devices.

“Input image” herein refers to an image that is to be processed for display onto a wide-gamut display device. The input image is typically represented in a color space having a gamut that is narrower than the gamut of the wide-gamut display device. For example, the input image is represented in standard color space.

“Standard color space” herein refers to the sRGB color space or a color space having a gamut having approximately the same size as the gamut of sRGB.

“Wide-gamut display device” herein refers to an electronic display device configured to display colors within a gamut that is substantially greater than the standard color space. Examples of wide-gamut display devices include OLED display, quantum dot display and laser projectors.

Referring now to FIG. 1 , therein illustrated is a schematic diagram of the operational modules of a system 100 for white balancing/gamut expansion according to various exemplary embodiments.

The white balancing/gamut-expansion system 100 includes a settings module 108 for receiving settings relevant to white balancing and/or gamut-expanding a received input image.

The settings module 108 may receive the relevant settings from a calibration environment intended to capture entry of user-related settings. The settings module 108 may also receive relevant settings already stored at a user device (ex: computer, tablet, smartphone, handheld console) that is connected to or has embedded thereto the wide-gamut display device. The settings module 108 may further receive relevant settings from an external device over a suitable network (ex: internet, cloud-based network). The external device may belong to a third party that has stored information about a user. The third party may be an external email account or social media platform.

The white balancing/gamut-expansion system 100 also includes a color scaling factors calculation module 116. The color scaling factors calculation module 116 receives one or more user-related settings from the settings module 108 and determines color scaling factors that are effective to apply white balancing within processing of the input image.

The color scaling factors calculation module 116 operates in combination with the white balancing module 124, which receives the calculated color scaling factors and applies the color scaling factors to cause white balancing (ex: shifting of white point).

The white balancing/gamut-expansion system 100 further includes a gamut-mapping module 132. The gamut-mapping module 132 is operable to map an image represented in a standard color space (ex: RGB, sRGB) to a color space having a wider gamut.

An output of the white balancing/gamut-expansion system 100 is a white-balanced, gamut-expanded image. The white-balancing and/or gamut expansion of the input image may be performed according to settings received by the settings module 108. It will be understood that gamut-mapping and gamut-expansion, and variants thereof are used interchangeably herein to refer to a process of mapping the colors of an input image represented in one color space to another color space.

Referring now to FIG. 2 , therein illustrated is a flowchart of the operational steps of an exemplary method 200 for processing an input image for display on a wide-gamut display device. The gamut of the wide-gamut display device may be known. Furthermore, the identity and/or characteristics of the user viewing the wide-gamut display device may also be known.

At step 208, an input image to be processed is received. As described elsewhere herein, the input image is represented in a color space that is narrower than available gamut of a wide-gamut display device. The processing of the input image seeks to alter the colors of the input image so that its color space covers a larger area of the gamut of the wide-gamut display device.

At step 216, one or more user-related characteristics of the user is received. The user-related characteristics refer to characteristics that may affect how the user perceives colors.

The user-related characteristics may include an age-related characteristic, such as the user's actual age, the user's age group, user's properties, preferences or activities (ex: browsing history) that may indicate an age of user, or a user-selected setting that corresponds to an effective age of the user. The user's age-related characteristic may be obtained from user details stored on the user-operated device that includes the wide-gamut display device. The user's age-related characteristics may be obtained from user accounts associated to the user, such as user information provided to an online service (ex: email account, third party platform, social media service).

In one example, a calibration/training phase may be carried out in which calibration images (ex: image of human faces) and a graphical control element are displayed to the user. Interaction of the graphical control element (ex: a slider) allows the user to select an effective age setting and the calibration images are adjusted according to how a typical user of that effective age would perceive the image. The user can then lock in a preferred setting, which becomes the effective age for that user. Accordingly, the age-related characteristic is a user-entered parameter.

In one example, the graphical control element is a slider and as the slider being controlled and the calibration images are being adjusted, the current effective age corresponding to the position of the slider is hidden from the user and not explicitly displayed. Accordingly, the user will not be influenced to choose an effective age that corresponds to the user's actual age. Sliders may also be used to let a user select other viewing characteristics, such as level of detail, color temperature and contrast.

Other user-related characteristics that affect user perception may include color-blindness of the user and ethnicity of the user.

At step 224, a color temperature setting is optionally received. The color temperature setting corresponds to a target color temperature for processing the input image. The color temperature setting may correspond to a preferred color temperature of the user. The color temperature setting may be entered by the user, for example, by selecting from a plurality of preset settings. The color temperature setting may be obtained from user-related properties, such as time of day or user location (users in different territories, such as different continents, typically have varying preferences for color temperatures).

In one example, a calibration/training phase may be carried out in which calibration images (ex: image of human faces) and a graphical control element are presented to the user so that the user can select a preferred color temperature. Interaction of the graphical control element (ex: a slider) allows the user to select an effective color temperature setting and the calibration images are adjusted according to the currently selected color temperature setting. The user can then lock in the preferred color temperature setting.

In one example, the effective age setting and the color temperature setting may be selected by the user within the same calibration/training environment in which the calibration images are displayed with two separate slides corresponding to the effective age setting and the color temperature setting respectively. The user can toggle both sliders to select a preferred effective age setting and color temperature setting to be used for processing the input image.

At step 232, a set of color scaling factors is determined based on the user-related characteristic, such as the age-related characteristic of the user, and based on the gamut of the wide-gamut display device. Determination of the set color scaling factors may also depend on the color temperature setting for the user. For example, the gamut of the wide-gamut display device may be represented by the primary spectra of the wide-gamut display device (i.e. the spectrum of each of the primary colors of the wide-gamut display device). The color scaling factors are effective for shifting the white point of an image.

At step 240, gamut-mapping is applied to the input image to generate a gamut-mapped image. The gamut mapping is applied based on the gamut of the wide-gamut display device.

At step 248, the color scaling factors are applied to shift the white point. In the illustrated example, the color scaling factors are applied to the input image after it has undergone gamut-mapping. Alternatively, the color scaling factors may be applied prior to the input image undergoing gamut-mapping.

A white-balanced, gamut-expanded version of the input image is outputted from the method and is ready for display on the wide-gamut display device of the electronic device being by the user.

Referring now to FIG. 3 , therein illustrated is a flowchart of the operational steps of an exemplary method 300 for determining color scaling factors for shifting a white point within processing of an input image. The method 300 may be carried out as a stand-alone method. Alternatively, steps thereof may be carried out within the method 200 for processing an input image for display on a wide-gamut display device.

For example, step 208 of receiving an input image, step 216 of receiving one or more user-related characteristics of the user and step 224 of receiving target color temperature of method 300 are substantially the same as the corresponding steps of method 200.

At step 250, the LMS cone responses for the age defined by the age-related characteristic are determined. The LMS cone responses may be determined based on known physiological model, such as the CIE-2006 physiological model [Stockman & Sharpe 2006].

At step 252, the black body spectrum for the received color temperature setting is determined.

At step 254, a first subset of age-based LMS cone responses to the black body spectrum is determined. This first subset of age-based LMS cone responses is determined using the set of LMS cone responses determined at step 250.

At step 256, a second subset of age-based LMS cone responses to the primary spectra of the wide-gamut display device is determined. This second subset of age-based LMS cone responses is also determined using the set of LMS cone responses determined at step 250.

At step 258, a set of color scaling factors that provides a correspondence between the first subset of LMS cone responses and the second subset of LMS cone responses is determined. The set of color scaling factors is effective for adjusting a white balance of an image, such as the white balance of an input image that has undergone gamut expansion.

The combination of steps 250 to 258 may represent substeps of step 232 of determining the set of color scaling factors of method 200.

It will be appreciated that the set of color scaling factors are determined taking into account LMS cone responses for the age defined by the age-related characteristic of the user. Accordingly, age-based white balancing is carried out. In other examples, the LMS cone responses may be determined taking into account LMS cone responses for another user-related characteristic, such as color-blindness and/or ethnicity.

According to various example embodiments, the method 300 of determining color scaling factors further includes balancing the primary spectra of the wide gamut display device according to a current white point (ex: white balancing setting) of the wide gamut display device. Furthermore, the second set of LMS cone responses may be determined based on the balanced primary spectra.

Balancing the primary spectra of the wide gamut display device includes measuring the actual output of the wide gamut display device to determine the actual white point of the display device. The primary spectra for the display device is then adjusted according to that white point for the purposes of determine the set of color scaling factors.

According to various example embodiments, the method 300 may further comprise normalizing the set of color scaling factors.

Referring now to FIG. 4 , therein illustrated is a flowchart of the operational steps of an exemplary method 400 for applying gamut mapping to an input image within processing of the input image. The method 400 may be carried out as a stand-alone method. Alternatively, steps thereof may be carried out within the method 300 for processing an input image for display on a wide-gamut display device.

At step 408, the color value components of pixels of the input image are converted to a chromaticity coordinate space.

At step 416, a sacred region is defined within the chromaticity coordinate space. As described further herein, the boundaries of the sacred region define how a set of color value components within the first color space of the input image will be mapped.

“Sacred region” herein refers to a region corresponding to colors that should remain unshifted or be shifted less than other colors during gamut-mapping because shifting of such colors has a higher likelihood of being perceived by a human observer as being unnatural. For example, colors falling within the sacred region may include neutral colors, earth tones and flesh tones.

At step 424, the color values of the input image are mapped according to the relative location of a set of color value components in the chromaticity space relative to the sacred region.

According to one example embodiment, for a given set of color value components, if the chromaticity coordinates corresponding to the set of color value components of the input image is located within the sacred region, a first mapping of the color value components is applied. If the chromaticity coordinates corresponding to the given set of color value components is located outside of the sacred region, a second mapping of the color value components is applied.

In one example, the input image is represented in a first color space and the wide-gamut display device is configured to display images in a second color space that is different than the first color space. A given set of color value components of the input image is converted to a corresponding set of color value components in a second color space. If the chromaticity coordinates corresponding to the given set of color value components of the input image falls within the sacred region, the first mapping is applied in which the set of color value components converted into the second color space of the wide-gamut display device is set as the output color value components of the gamut-mapped output image.

In one example, if the chromaticity coordinates of a given set of color value components is located outside the sacred region, the second mapping is applied based on a distance between the chromaticity coordinates and an edge of the sacred region. The second mapping is further based on a distance between the chromaticity coordinates and an outer boundary of the second color space defining the spectrum of the wide-gamut display device. The outer boundary of the second color space corresponds to the chromaticity coordinates of the primaries of the wide-gamut display device.

A linear interpolation between the color value components in the first color space and the color value components in the second color space may be applied. The linear interpolation may be based on a ratio of the two distances calculated.

It will be understood that method 400 may be carried out on a pixel by pixel basis for the input image, wherein the steps of method 400 are repeated for each image pixel. That is, the color value components of a given pixel are converted to the chromaticity space and the mapping is carried to determine the color value components in the second color space for the specific pixel. It will be further understood that the sacred region may be defined in the chromaticity space prior to gamut-mapping each of the pixels of the input image.

Referring now to FIG. 5 , therein illustrated is a system 500 for displaying standard color space content on a display device 508 of a user device 516 (ex: computer, tablet, smartphone, handheld console) currently being used by a user. The user device 516 is configured to execute a computer program 520 in which various graphical content is to be displayed on the wide-gamut display device 508. The computer program may be an application or “app” executing in a particular environment, such as within an operating system. Alternatively, the computer program may be an embedded feature of the operating system.

The graphical content may be generated by a content generating party 524. The graphical content may be one or more images and/or videos. The content generating party 524 is in communication with the user device 516 running the computer program over a suitable network, such as the Internet, WAN or cloud-based network. The content generating party 524 may include a content selection module 528 that selects the graphical content to be displayed by the computer program. The content selection module 528 may receive from the computer program 520 information about the user (ex: user profile, user history, etc.) and generate content-adapted to the user profile.

The selected graphical content is received at the computer program 520. However, where the graphical content is in standard color space, the display of the graphical content may not be suitably adapted to the user viewing that content via the display device 508. The received graphical content is processed by an image processing module 532 implemented within the user device 516 to generate a processed graphical content adapted to the user.

For example, the image processing may include gamut-mapping the graphical content for display on wide-gamut display device according to method described herein. Additionally, or alternatively, the gamut-mapping may include white-balancing, contrast adjustment, tone-mapping, adjustment for color blindness, sensitivity adjustment, limiting brightness, etc.

The image processing module 516 is configured to receive a user-related characteristic of the user using the electronic device 516. The user-related characteristic of the user may be stored on the electronic device 516. Alternatively, the user-related characteristic of the user may be received from a third party provider 540, such as over a suitable communication network. The third party provider 540 may be an email account or social media platform that has an account associated to the user. Account information or use of the social media platform can include user-related characteristics of the user.

The image processing module 532 is configured to receive an user-related characteristic of the user using the electronic device 516. The user-related characteristic of the user may be stored on the electronic device 516.

Based on the user-related characteristics of the user, the image processing module 532 performs processing of the graphical content. Additionally, or alternatively, the image processing module 532 may perform the processing of the graphical content based on ambient viewing characteristics and/or device-related characteristics. The image processing module 532 may perform processing methods developed by Irystec Inc. that improve user perception of the graphical content. These processing methods may include the gamut-mapping described herein according to various example embodiments, adjusting for ambient lighting conditions (ex: luminance retargeting, contrast adjustment, color retargeting transforming an image according to peak luminance of a display), video tone mapping, etc. Image processing techniques may include methods described in PCT application no. PCT/GB2015/051728 entitled “IMPROVEMENTS IN AND RELATING TO THE DISPLAY OF IMAGES”; PCT application no. PCT/CA2016/050565 entitled “SYSTEM AND METHOD FOR COLOR RETARGETING”; PCT application no. PCT/CA2016/051043 entitled “SYSTEM AND METHOD FOR REAL-TIME TONE-MAPPING”, U.S. provisional application No. 62/436,667 entitled “SYSTEM AND METHOD FOR COMPENSATION OF REFLECTION ON A DISPLAY DEVICE”, all of which are incorporated herein by reference.

Ambient viewing characteristics refer to characteristics defining the ambient conditions present within the environment surrounding the electronic device 516 and which may affect the experience of the viewer. Such ambient viewing characteristics may include level of ambient lighting (ex: bright environment vs dark environment), presence of a light sources causing reflections on the display device, etc. The ambient viewing characteristics can be obtained using various sensors of the electronic device, such as GPS, ambient light sensor, camera(s), etc.

Device-related characteristics refer to characteristics defining capabilities of the electronic device 516 and which may affect the experience of the viewer. Such device-related characteristics may include resolution of the display device 508, type of the display device 508 (ex: LCD, LED, OLED, VR display, etc.), gamut of the display, processing power of the electronic device 516, current workload of the electronic device 516, peak luminance of the display, current mode of the display (ex: power saving mode) etc.

The gamut-mapped graphical content is passed to the computer program 520 and the program 524 causes the processed graphical content 536 to be displayed on the display device 508 of the user electronic device.

One of the image processing module 532 and the computer program 520 may further transmit to the content generating party 524 a message indicating that the graphical content was gamut-mapped prior to being displayed on the display device 508 of the user device 516.

In some example embodiments, the graphical content may be an interactive element, such as advertising content. The computer program 520 monitors the graphical content to detect user interaction with the graphical content (ex: selecting, clicking, scrolling to, sharing, viewing by user) and transmits a message indicating the gamut-mapped graphical content was interacted with by the user.

The content generator 524 may further include a playback tracking module 548 that tracks the amount of times a graphical content was processed by the image-processing module and/or the amount of times the processed graphical content was interacted with.

The image processing module 532 may be implemented separately from the computer program 520 being used by the user. Alternatively, the image processing module 532 is embedded within the computer program 520.

In some example embodiments, the image processing module 532 may be implemented within the content generator 524. Accordingly, the content generator 524 receives user-related characteristics, ambient viewing characteristics and/or device-related characteristics from the electronic device 516 and processes the selected content based on these characteristics prior to transmitting the content to the electronic display 516 for display.

For example, the user-related characteristic is a perception-related characteristic, such an age-related characteristic of the user and the image processing includes white/balancing and gamut-mapping according to various examples described herein.

It will be appreciated that the image processing module 532 causes the graphical content to be further processed so as to improve viewer perception of the graphical content. Furthermore, the processing is personalized to one or more specific characteristics of the user that directly influence viewer perception.

Referring now to FIG. 6 , therein illustrates is a flowchart of the operational steps of an example method 600 for user-adapted display of graphical content from a content provider.

At step 608, the graphical content to be displayed is received, such as from the third party content provider.

At step 616, user-related characteristic is received. Ambient viewing characteristics and/or device-related characteristics may also be received.

At step 624, the graphical content is processed for display based on the received user-related characteristic, ambient-viewing characteristics and/or device-related characteristics.

At step 632, the processed graphical content is displayed to the user.

At step 640, interaction of the processed graphical content is monitored and detected. One or more notifications may be further transmitted to indicate such interactions. The interaction of the user with the electronic device displaying the processed graphical content may be monitored and detected by the electronic device and the notification is transmitted to the content generating party 524 or a third party. The notification provides an indicator of the selected graphical content that was displayed, that the graphical content had been processed for improved perception, and that the processed content had been interacted with.

For example, the graphical content can be an advertising content and processing the graphical content seeks to attract the attention of the user. The notification indicates a “click-through” by the user. The content generating party or third party receiving the notification tracks the number of interactions that occur. Such information pertaining to notifications may be used to determine an amount of compensation for the service of processing the graphical content.

In a real-life example, a user may be accessing content online such as via a website, mobile app, social media service, or content-streaming service. Graphical content, such as an advertisement is selected for user to be displayed with the content. For example, the party generating the online content can also select the advertisement to be displayed. The user-related characteristics, ambient viewing characteristics, and device-related characteristics can be obtained. The user-related characteristics can be obtained from user profile information stored on the electronic device or from one or more social media profiles for that user. The graphical content is then processed to improve perceptual viewing for the user and displayed with the online content. As the user is consuming the online content, the user's activities are monitored to detect if the user interacts with the processed graphical advertisement content. If the user interacts with the processed graphical advertisement content, a notification is emitted indicating that the graphical content was processed and that the user interacted with it.

Example Implementation

An example implementation includes a white balancing technique that allows for observer variation (metamerism) together with color temperature preference, and a gamut expansion technique that maps the sRGB input to the wider OLED gamut while preserving the accuracy of critical colors such as flesh tones.

The CIE 2006 model of age-based observer color-matching functions was employed, which establishes a method for computing LMS cone responses to spectral stimuli [Stockman & Sharpe 2006]. This model was used to discover the range of expected variation rather than predict responses from age alone. Differences in color temperature preference were also allowed, as it has been shown that some users prefer lower (redder) or higher (bluer) whites than the standard 6500° K [Fernandez & Fairchild 2002]. A user will be shown a set of faces on a neutral background and offered a 2-axis control to find theft preferred white point setting, which corresponds to the age-related and color temperature-related dimensions. (Age variations tend along a curve from green to magenta; while color temperature varies from red to blue, so overall this provides ample variation.) The Radbound Faces Database [Langner et al. 2010] was used.

A common method to utilize the full OLED color gamut is to map RGB values directly to the display, which results in saturated but inaccurate colors. The precise mapping of sRGB to an OLED display using an appropriate 3×3 transform eliminates any benefit from the wider gamut, as it restricts the output to the input (sRGB) color range.

It was observed that accuracy is most important in the neutral and earth-tone regions of color space, where shifts and excessive saturation may be objectionable. Out towards the spectral locus, however, over-saturated colors may be desirable, since observers are less critical of variations in the saturation or even the hue of strong colors. Especially for naïve viewers, brilliant colors are frequently favored over accurate ones.

The example implementation seeks to preserve the accuracy of colors in an identified “sacred” region of color space, which is to be determined but will include all variations of flesh tones and commonly found earth tones. Outside this region, the mapping is gradually altered to where values along the sRGB gamut boundary map to values along the target OLED display's maximum gamut.

The example implementation takes a sRGB input image and maps it to an AMOLED display using a preferred white point, and maintaining accuracy in the neutrals while saturating the colors out towards the gamut boundaries. The details of the implementation and some example output are given below.

An important step in the implementation is to adjust the display white point to correspond to the viewer's age-related color response and preference. The two inputs are CIE-2006 observer age and black body temperature. As described, the two inputs may be obtained from a user given 2-dimensional control of control elements representing effective age and color temperature where the actual effective age value and color temperature is hidden from the user. From these parameters and detailed measurements of the OLED RGB spectra and default white balance, the white balance multipliers (color scaling factors) are calculated using the following procedure:

-   -   1. Balance OLED primary spectra so they sum to current display         white point. (I.e., multiply against 1931 standard observer         curves and solve for RGB scaling that achieve measured         xy-chromaticity.) An arbitrary scale factor corresponding to         maximum white luminance will remain, which does not matter in         this context.     -   2. Determine the LMS cone responses for the given age based on         the CIE-2006 physiological model.     -   3. Compute the black body spectrum for the specified target         color temperature.     -   4. Compute the age-based LMS cone responses to this black body         spectrum.     -   5. Compute the 3×3 matrix corresponding to the LMS cone         responses to the OLED RGB primary spectra.     -   6. Solve the linear system to determine the RGB factors (again         within a common luminance scaling) that achieve the desired         black body color match.     -   7. Divide these white balance factors by the maximum of the         three, such that the maximum factor is 1. These are the linear         factors to be applied to each RGB pixel to map an image to the         desired white point.

Note that there are two degrees of freedom on the input, age and color temperature, and two degrees of freedom in the output, since one of the RGB factors is always 1.0.

FIG. 7 illustrates the difference in D65 white appearance relative to a 25 year-old reference subject on a Samsung AMOLED display (Galaxy Tab) for 2 degree and 10 degree patches.

For gamut-mapping, it is assumed that information about the larger gamut has been lost in the capture or creation of the sRGB input image, thus the correct representation cannot be deduced to fully utilize the wide-gamut display device's full color range. Rather than maintaining the smaller sRGB gamut on the wider gamut of the wide-gamut display device, gamut-mapping seeks to expand into a larger gamut in a perceptually preferred manner.

The example implementation seeks a gamut-mapping that is straightforward, while achieving the following goals:

-   -   1) Unsaturated colors in the critical region of color space,         i.e., earth- and flesh-tones, must be untouched (i.e,         colorimetric).     -   2) The most saturated colors possible in sRGB should map to the         most saturated colors in the destination gamut, achieving an         infective function (one-to-one mapping) between gamut volumes.     -   3) Luminance and the associated contrast should be preserved.

The gamut-mapping according to the example implementation starts by defining a region in color space where the mapping will be strictly colorimetric, and assume this is wholly contained within both source and destination gamuts. This region corresponds to the sacred region, which is defined as a point in CIE (u′,v′) color space and a radial function surrounding it. For the example implementation, a central position of (u′,v′)=(0.217,0.483) with a constant radius of 0.051 based on empirical measurements of natural tones. (This center might be further tuned or adjusted, and a more sophisticated radial function employed in future.)

The injective gamut-mapping function is defined as follows. For colors falling inside the defined sacred region, values are mapped colorimetrically (TCM), reproducing them as closely as possible to the original sRGB values on the target wide-gamut display device. This linear 3×3 mapping matrix is called M_(d). Thus: RGB _(d) ^(T) =M _(d) RGB _(i) ^(T) where:

-   -   RGB_(i)=linearized input values in CCIR-709 primaries         -   RGB_(d)=linear colorimetric display drive values

The white point may be transformed as well by the above matrix to match the source white point to that of the display. Linearized input colors are mapped to CIE XYZ using the matrix M_(x) then to (u′,v′) using the following standard formulae:

XYZ_(i)^(T) = M_(x)RGB_(i)^(T) u^(′) = 4X/(X + 15Y + 3Z) v^(′) = 9Y/(X + 15Y + 3Z) ${\text{where}:\mspace{14mu} M_{x}} = \begin{bmatrix} 0.497 & 0.339 & 0.164 \\ 0.256 & 0.678 & 0.066 \\ 0.023 & 0.113 & 0.864 \end{bmatrix}$

(The M_(x) matrix deliberately leaves off the D65 white point conversion, since the viewer is adapted to display white and the center of the sacred region should be maintained.)

For input colors outside the sacred region, the example implementation interpolates between the colorimetric mapping above and an SDS mapping that sends the original RGB_(i) values to the display, applying linearity (“gamma”) correction to each channel as needed.

FIG. 8 shows the sacred region (green) with line drawn from center through input color to sRGB gamut boundary. The sacred region is shown in green, and the red line drawn from the center to the sRGB gamut boundary represents an approximation to constant hue. The distance a is how far the input color is from the edge of the sacred region in (u′,v′) coordinates. The distance b is the distance from the edge of the sacred region to the sRGB gamut boundary along that hue line. The value d is the ratio of a/b. The linear drive value is then computed as: RGB ₀=(1−d ²)·RGB _(d) +d ² RGB _(i)

It was observed that a power of d was preferred over the more commonly used linear interpolant, although the results were not overly sensitive to the acceleration factor. This differs from previous blending factors for HCM, which apply a linear ramp keyed on saturation rather than distance between a sacred region and the gamut boundary. The power function provides functional continuity and better preserves “almost sacred” colors.

The effect of this mapping on a regular array of (u′,v′) chromaticity coordinates is shown in FIG. 9 , where sRGB is mapped to a particular set of AMOLED primaries. Note that there is little to no motion in the central portion defined as the sacred region. Even in the more extreme case of the laser primaries shown in FIG. 10 , neutral colors are mapped colorimetrically. However, more saturated colors are expanded out towards the enlarged gamut boundary, even rotating hue as necessary to reach the primary corners. One hypothesis is that observers are less sensitive to color shifts at the extremes, so long as general relationships between color values are maintained. Interpolating between colorimetric and direct drive signal mappings maximizes use of the destination gamut without distorting local relationships. The third dimension (luminance) is not visualized, as it does not affect the mapping. Values that were clipped to the gamut boundary in sRGB will be clipped in the same way in the destination gamut; this is an intended consequence of the hybrid color mapping (HCM) method.

FIG. 11 shows (to the extent possible) the color shifts seen when expanding from a sRGB to laser primary color space using the example implementation. Unsaturated colors match between the original and the wide-gamut display device, while saturated colors become more saturated and may shift in hue towards the target device primaries.

Experimental Validation of Gamut-Mapping Implementation

The performance of gamut-mapping model of the experimental implementation was evaluated using the pairwise comparison approach introduced in [Eilertsen]. The experiment was set up in a dark room with a laser projector (PicoP by MicroVision Inc.) having a wide gamut color space shown in FIG. 10 . 10 images processed by 3 different color models, the implemented HCM gamut mapping, colorimetric or true color mapping-TCM, and original image—SDS (same drive signal) were used. 20 naïve observers were asked to compare the presented result. Observers were asked to pick their preferred image of the pair. For each observer, total 30 pairs of images were displayed using the laser projector, 10 pairs for TCM:HCM, 10 pairs for HCM:SDS, and 10 pairs for SDS:TCM. The observers were instructed to select one of the two displayed images as their preferred image based on the overall feeling of the color and skin tones.

Observers consist of 7 females and 13 males from the age of 20 to 58. On average, the whole experiment took about 10 minutes for each observer.

FIG. 12 shows gamut mapping results (HCM) with original images (SDS) and colorimetric mapping (TCM). A few of the images include well-known actors whose skin tones may be familiar to the observers. The example gamut mapping result keeps the face and skin color as in the colorimetric reference, but represents other areas more vividly, such as the colorful clothes in the image Wedding (1_(st) row, left), the tiger balloon in the image Girl (4_(th) row, right), and the red pant of a standing boy in the image Family (5_(th) row, right).

The pairwise comparison method with just-noticeable difference (JND) evaluation was used in the experiment. This approach has been used recently for subjective evaluation in the literature [Eilertsen, Wanat, Mantiuk]. The Bayesian method of Silverstein and Farrell was used, which maximizes the probability that the pairwise comparison result accounts for the experiment under the Thurstone Case V assumptions. During an optimization procedure, a quality value for each image is calculated to maximize the probability, modeled by the binomial distribution. Since there are 3 conditions for comparison (HCM, TCM, SDS), this Bayesian approach is suitable, as it is robust to unanimous answers and common when a large number of conditions are compared.

FIG. 13 shows the result of the subjective evaluation calculating the JND values as defined in [Eilertsen]. The absolute JND values are not meaningful by themselves, since only relative difference can be used for discriminating choices. A method with higher JND is preferred over methods with smaller JND values, where 1 JND corresponds to 75% discrimination threshold. The FIG. 13 represents each JND value for each scene, rather than the average value, because JND is a relative value that can be also meaningful when compared with others. FIG. 13 represent the confidence intervals with 95% probability for each JND. To calculate the confidence intervals a numerical method was used, known as bootstrapping which allows estimation of the sampling distribution of almost any statistic using random sampling method [18]. 500 random sampling were used, then computed 2.5th and 97.5th percentiles for each JND point. The reason why JND values of SDS are same is that both JND and confidence intervals for JND are relative values. A reference point is needed to calculate them. SDS was chosen as the reference point. For 7 of the images, the example mapping is the most preferred method, with JND differences of 0.03-1.8 between it and the second most preferred method. For 3 of the images (Anthony, Family, George), HCM is not the most preferred method, losing by JND differences of 0.31-0.71.

Validation of Processing Graphical Content in the Context of Advertisement

A comparative advertisement campaign was carried out over the Facebook™ social networking platform in which two static advertisement banners were displayed on the Facebook™ platform. Each banner included a photograph of a human and some text. Each advertisement banner was displayed as originally created in some instances and was displayed in some instances after being processed to improve user perception. It was observed that for the first banner, the click-through rate for the unprocessed version was 1.85% while the click-through rate for the processed version was 2.92%. It was also observed that for the second banner, the click-through rate for the unprocessed version was 4.23% while the click-through rate for the processed version was 4.97%.

A second comparative advertisement campaign was carried out over Facebook™ social networking platform in which a 30 second video advertisement was displayed. It was observed that the click-through rate for the unprocessed version was 1.02% while the click-through rate for the processed version was 1.32%.

It will be appreciated that in both campaigns, processing the advertisement content resulted in a higher click-through rate versus the unprocessed version of the advertisement content.

Several alternative embodiments and examples have been described and illustrated herein. The embodiments of the invention described above are intended to be exemplary only. A person skilled in the art would appreciate the features of the individual embodiments, and the possible combinations and variations of the components. A person skilled in the art would further appreciate that any of the embodiments could be provided in any combination with the other embodiments disclosed herein. It is understood that the invention may be embodied in other specific forms without departing from the central characteristics thereof. The present examples and embodiments, therefore, are to be considered in all respects as illustrative and not restrictive, and the invention is not to be limited to the details given herein. Accordingly, while specific embodiments have been illustrated and described, numerous modifications come to mind without significantly departing from the scope of the invention as defined in the appended claims.

REFERENCES

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The invention claimed is:
 1. A method for processing an input image having an initial gamut into a targeted image having a targeted gamut which is wider than said initial gamut for display on a wide-gamut display device having an available gamut which is wider than said initial gamut, said input image having color value components of pixels, the method comprising: receiving said input image; determining a set of color scaling factors based on one or more user-related characteristics of a user of said wide-gamut display device and said available gamut of the wide-gamut display device, wherein the one or more user-related characteristics of the user include an age-related characteristic, and wherein the set of color scaling factors is determined based on LMS cone responses for an age defined by the age-related characteristic; applying a gamut-mapping to the input image, based on said available gamut of the wide-gamut display device, to generate a gamut-mapped image; and applying the set of color scaling factors to the gamut-mapped image to generate said targeted image, wherein the set of color scaling factors are effective for adjusting a white balance of the gamut-mapped image.
 2. The method of claim 1, wherein said age-related characteristic is one of the following: an actual age of the user; an age group of the user; properties, preferences or activities indicative of an age of the user; and a user-selected setting that corresponds to an effective age of the user.
 3. The method of claim 2, wherein said user-related characteristics are obtained from user details stored on a user-operated device that includes the wide-gamut display device, or from one or more online user accounts associated with the user.
 4. The method of claim 2, wherein said determining the set of color scaling factors comprises: determining the LMS cone responses for the age defined by the age-related characteristic, based on known physiological model; determining a black body spectrum for a received color temperature setting; determining a first subset of age-based LMS cone responses to said black body spectrum, by using the determined set of LMS cone responses; determining a second subset of age-based LMS cone responses to a primary spectra of the wide-gamut display device, by using the determined set of LMS cone responses; and determining the set of color scaling factors that provides a correspondence between the first subset of LMS cone responses and the second subset of LMS cone responses.
 5. The method of claim 4, wherein said determining the set of color scaling factors comprises: balancing the primary spectra of the wide gamut display device according to a current white point of the wide gamut display device.
 6. The method of claim 1, wherein said user-related characteristics are obtained from a calibration/training phase in which calibration images and a graphical control element are displayed to the user, and in which an interaction of the user with said graphical control element is used to select an effective age setting, said calibration images being adjusted according to the effective age setting.
 7. The method of claim 1, wherein said user-related characteristics further comprise color-blindness of the user and ethnicity of the user.
 8. The method of claim 1, wherein said set of color scaling factors are further based on a target color temperature corresponding to a target color temperature for processing the input image.
 9. The method of claim 1, wherein said applying the gamut-mapping to the input image, comprises: converting the color value components of the pixels of the input image to a chromaticity coordinate space; defining a sacred region within the chromaticity coordinate space; mapping color values of the input image according to a relative location of a set of color value components in a chromaticity space relative to the sacred region.
 10. The method of claim 9, wherein said mapping color values of the input image having a given set of color value components, comprises: applying a first mapping, if chromaticity coordinates corresponding to the given set of color value components of the input image is located within the sacred region; and applying a second mapping, if the chromaticity coordinates corresponding to the given set of color value components is located outside of the sacred region.
 11. The method of claim 10, wherein said second mapping is applied based on a first distance between the chromaticity coordinates corresponding to the given set of color value components and an edge of the sacred region, and based on a second distance between the chromaticity coordinates corresponding to the given set of color value components and an outer boundary of a color space defining a spectrum of the wide-gamut display device.
 12. The method of claim 9, wherein said applying the gamut-mapping to the input image is carried out on a pixel by pixel basis for the input image, and is repeated for each image pixel.
 13. A user device comprising: a wide gamut display device configured for displaying images; at least one processor; and memory storing a computer program, said at least one processor being configured to execute said computer program in order to generate a processed graphical content adapted to a user; and wherein the at least one processor is configured to perform the image processing method of claim 1 for generating said processed graphical content for display on said wide-gamut display device as a result of executing the computer program.
 14. The user device of claim 13, wherein said computer program comprises an application executing in a particular computing environment.
 15. The user device of claim 14, wherein said various graphical content is received at the user device over a long range computer network.
 16. The user device of claim 15, said various graphical content having been selected or generated based on information about the user previously sent from the computer program over the long range computer network.
 17. The user device of claim 13, wherein said computer program is configured for monitoring the various graphical content to detect the user's interaction with the various graphical content and for transmitting a message indicating the processed graphical content is interacted with by the user. 